from grid_env_ideal_obs_repeat_task import *
from grid_agent import *
from checkpoint_utils import *
from maze_factory import *
from replay_config import *
import argparse
import json
import sys
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.lines import Line2D
from sklearn.manifold import TSNE
import random
from sklearn.decomposition import PCA
from matplotlib.animation import FuncAnimation
from sklearn.cluster import KMeans
import threading
import mplcursors
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

def progress_bar(current, total, barLength = 100):
    percent = float(current) * 100 / total
    arrow = '-' * int(percent/100 * barLength - 1) + '>'
    spaces = ' ' * (barLength - len(arrow))

    print('Progress: [%s%s] %d %%' % (arrow, spaces, percent), end='\r')
    sys.stdout.flush()

@partial(jax.jit, static_argnums=(3,))
def model_forward(variables, state, x, model):
    """ forward pass of the model
    """
    return model.apply(variables, state, x)

@jit
def get_action(y):
    return jnp.argmax(y)
get_action_vmap = jax.vmap(get_action)

# load landscape and states from file
def load_task(pth = "./logs/task.json", display = True):
    # open json file
    with open(pth, "r") as f:
        data = json.load(f)
        landscape = data["data"]
        state = data["state"]
        goal = data["goal"]
        if display:
            print("state: ", state)
            print("goal: ", goal)
            print("landscape: ", landscape)
    return landscape, state, goal


def main():

    seq_len = 15
    redundancy = 5
    diverse_set_capacity = 5

    """ parse arguments
    """
    rpl_config = ReplayConfig()

    parser = argparse.ArgumentParser()
    parser.add_argument("--model_pth", type=str, default=rpl_config.model_pth)
    parser.add_argument("--map_size", type=int, default=rpl_config.map_size)
    parser.add_argument("--task_pth", type=str, default=rpl_config.task_pth)
    parser.add_argument("--log_pth", type=str, default=rpl_config.log_pth)
    parser.add_argument("--nn_size", type=int, default=rpl_config.nn_size)
    parser.add_argument("--nn_type", type=str, default=rpl_config.nn_type)
    parser.add_argument("--show_kf", type=str, default=rpl_config.show_kf)
    parser.add_argument("--visualization", type=str, default=rpl_config.visualization)
    parser.add_argument("--video_output", type=str, default=rpl_config.video_output)
    parser.add_argument("--life_duration", type=int, default=rpl_config.life_duration)

    args = parser.parse_args()

    rpl_config.model_pth = args.model_pth
    rpl_config.map_size = args.map_size
    rpl_config.task_pth = args.task_pth
    rpl_config.log_pth = args.log_pth
    rpl_config.nn_size = args.nn_size
    rpl_config.nn_type = args.nn_type
    rpl_config.show_kf = args.show_kf
    rpl_config.visualization = args.visualization
    rpl_config.video_output = args.video_output
    rpl_config.life_duration = args.life_duration

    nn_type = ''
    if rpl_config.nn_type == "vanilla":
        nn_type = "vanilla"
    elif rpl_config.nn_type == "gru":
        nn_type = "gru"

    def load_data_and_compute(nn_type, seq_len, redundancy, diverse_set_capacity):

        rnn_limit_rings_file_name = "./logs/rnn_limit_rings_of_best_estimation_" + nn_type + "_" + str(seq_len) + "_" + str(redundancy) + "_" + str(diverse_set_capacity) + ".npz"
        # 载入 npz 文件
        rnn_limit_rings_of_best_estimation_file = np.load(rnn_limit_rings_file_name)

        # 获取 npz 文件中的所有对象名称
        matrix_names = rnn_limit_rings_of_best_estimation_file.files

        rnn_limit_rings_of_best_estimation = []

        # 遍历对象名称，访问和操作每个矩阵对象
        for name in matrix_names:
            matrix = rnn_limit_rings_of_best_estimation_file[name]
            # 在这里进行对矩阵对象的操作
            # 例如，打印矩阵的形状
            # print(f"Matrix '{name}' shape: {matrix.shape}")
            rnn_limit_rings_of_best_estimation.append(matrix)

        # 求 rnn_limit_rings_of_best_estimation 的中心位置序列
        rnn_limit_rings_of_best_estimation_center = []
        for i in range(len(rnn_limit_rings_of_best_estimation)):
            rnn_limit_rings_of_best_estimation_center.append(np.mean(rnn_limit_rings_of_best_estimation[i], axis=(0,1)))
        rnn_limit_rings_of_best_estimation_center = np.array(rnn_limit_rings_of_best_estimation_center)
        print("rnn_limit_rings_of_best_estimation_center.shape: ", rnn_limit_rings_of_best_estimation_center.shape)

        return rnn_limit_rings_of_best_estimation_center
    
    def load_trajectories(nn_type, seq_len, redundancy, diverse_set_capacity):

        file_name = "obs_data_" + nn_type + "_" + str(seq_len) + "_" + str(redundancy) + "_" + str(diverse_set_capacity) + ".npz"
        obs_file = np.load("./logs/" + file_name)
        diverse_set_trajectoies = obs_file["diverse_set_trajectoies"]

        return diverse_set_trajectoies

    configs = [
        
        [nn_type, 6, 1, 100],
        [nn_type, 7, 1, 100],
        [nn_type, 8, 1, 100],
        [nn_type, 9, 1, 100],
        [nn_type, 10, 1, 100],
        [nn_type, 11, 1, 100],
        [nn_type, 12, 1, 100],
        [nn_type, 13, 1, 100],
        [nn_type, 14, 1, 100],
        [nn_type, 15, 1, 100],
        
        ]

    rnn_limit_rings_of_best_estimation_centers = []
    trajectories = []
    for i in range(len(configs)):
        rnn_limit_rings_of_best_estimation_centers.append(load_data_and_compute(configs[i][0], configs[i][1], configs[i][2], configs[i][3]))
        trj = load_trajectories(configs[i][0], configs[i][1], configs[i][2], configs[i][3])
        for j in range(trj.shape[0]):
            trj[j] = trj[j,:] - trj[j,0]
        trajectories.append(trj)
        print("shape of trajectories: ", np.array(trajectories[i]).shape)

    # 将 rnn_limit_rings_of_best_estimation_centers 所有元素拼接起来
    rnn_limit_rings_of_best_estimation_center_mat = np.concatenate(rnn_limit_rings_of_best_estimation_centers, axis=0)

    # 对 rnn_limit_rings_of_best_estimation_center_mat 进行 PCA
    pca = PCA()
    pca.fit(rnn_limit_rings_of_best_estimation_center_mat)

    # 使用 PCA 对 rnn_limit_rings_of_best_estimation_centers 中的所有数据，逐个进行降维
    rnn_limit_rings_of_best_estimation_centers_pca = []
    for i in range(len(rnn_limit_rings_of_best_estimation_centers)):
        rnn_limit_rings_of_best_estimation_centers_pca.append(pca.transform(rnn_limit_rings_of_best_estimation_centers[i]))
    rnn_limit_rings_of_best_estimation_centers_pca = np.concatenate(rnn_limit_rings_of_best_estimation_centers_pca, axis=0)

    # 使用不同的颜色将 rnn_limit_rings_of_best_estimation_centers_pca 中的所有数据进行可视化
    fig = plt.figure()
    ax = fig.add_subplot(121, projection='3d')
    scatter = ax.scatter(rnn_limit_rings_of_best_estimation_centers_pca[:,0], rnn_limit_rings_of_best_estimation_centers_pca[:,1], rnn_limit_rings_of_best_estimation_centers_pca[:,2], s=5)

    # 获取X轴的范围
    x_min, x_max = rnn_limit_rings_of_best_estimation_centers_pca[:,0].min(), rnn_limit_rings_of_best_estimation_centers_pca[:,0].max()
    x_min, x_max = x_min*1.5, x_max*1.5
    # 设置Y轴和Z轴的范围与X轴一致
    ax.set_xlim(x_min, x_max)
    ax.set_ylim(x_min, x_max)
    ax.set_zlim(x_min, x_max)

    # 右边的画面 - 2D空白plot
    ax2 = fig.add_subplot(122)
    ax2.set_xlim(-20, 20)
    ax2.set_ylim(-20, 20)

    # 创建mplcursors对象
    cursor = mplcursors.cursor(ax, hover=True)

    # 定义回调函数，获取鼠标指向的数据点索引位置
    @cursor.connect("add")
    def on_add(sel):
        index = sel.target.index
        print("index: ", index)
        # 在 ax2 子图上绘制轨迹
        print(index // 100, index % 100)
        # 清除之前绘制的轨迹
        ax2.clear()
        ax2.plot(trajectories[index // 100][index % 100,:,0],trajectories[index // 100][index % 100,:,1])
        ax2.set_xlim(-20, 20)
        ax2.set_ylim(-20, 20)
        

    plt.show()


if __name__ == "__main__":
    main()